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Pose EstimationStrong background in computer vision and machine learning applied to pose estimation and visual servoing; Experience with OpenCV, PCL (Point Cloud Library), PyTorch/TensorFlow, and 3D
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for developing machine learning models for the automatic identification of species from images collected through electronic monitoring systems (Work Package 3 – Bycatch Monitoring). The candidate will be involved
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Area: Computer Science 2. Admission Requirements: Graduates (Licenciatura) in computer engineering or related area, with experience in Machine Learning/Deep Learning methods/techniques. 3. Project
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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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RE-C05-i08 do Programa de Recuperação e Resiliência, através da Fundação para a Ciência e a Tecnologia - FCT, nas seguintes condições: Scientific Area: Computer Engineering, Biomedical Engineering
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to facilitate the integration of the framework with external systems and educational platforms; Establish a Machine Learning Operations (MLOps) pipeline to automate the lifecycle of models, including training
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-C05-i08 of the Recovery and Resilience Program, through the Foundation for Science and Technology - FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering
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-C05-i08 of the Recovery and Resilience Program, through the Foundation for Science and Technology – FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering
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: • Backend based on REST/GraphQL APIs that expose cork stopper catalogue functionalities, creation and management of final products, and consultation of machine learning model records; • Angular frontend
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. This recommendation system should be designed based on current machine learning and artificial intelligence strategies, allowing it to adapt to each user's profile and the different types of data collected by